we can sell it to lots of users, we can sell it to ag customers, civil government customers and so on. And, every time, the incremental cost of selling our imagery to the next player is, of
course, really low. Its just the compute and egress costs of selling that image, so all the analytics feed on top of that. So its very, very high margins. 62% is already including, is for our PlanetScope business, versus 73% of our
revenue last year, and that includes the cost of the satellites. Most people think oh my god, you are building satellites and operating satellites that must be terrible gross margin, but that 62% is going up very fast and already
includes the cost of satellite, so weve really made very efficient satellite systems.
Just a couple more slides. Then, next slide is just the video
that just shows a little bit more how it works. So we have 180 satellites in a polar orbit, sun synchronous orbit its called, and they each take a set of images as they go down, just vertically a strip of image, but the Earth rotates underneath so
that by the time the next satellite comes it takes a strip next to the previous one, so they end up being like a line scanner for the Earth every day. Because the Earth rotates in 24-hours we scan the whole Earth. We also have a set of Skysats,
which are high-resolution images, that can have resolution up to 50 centimeters and can take images on any particular place. We just did this really cool thing last year, we launched two sets of three satellites on two Spacex rockets into two
inclined planes that enable us to revisit any particular location up to 12 times per day. Thats the fastest revisit system of any Earth-imaging system on the planet, or off the planet in this case. But anyway, thats roughly how our
system works.
You can go to the next slide. Thats a little bit about the backend and, of course, as a space geek, I could spend hours talking about
that, but Im not going to. In the interest of time, what I wanted to speak to very, very briefly, is that whats exciting is what does this mean for the customer. And what the customer looks at is something like this. Its like the
imagery served up, its a bit like Google Earth, but you have todays image and yesterdays image, and the day before, and you have over 1,000, actually over 1,500 images for every point on the Earth landmass on average. So, just
imagine a time series of imagery. And you can set up your bespoke areas of interest and time of interest and fees, analytical fees, you can say just show me the boats in the ports in my area or just show me the agricultural
output, and we have those sort of analytical feeds. And in the end, the customers are subscribing to these data feeds, whether in agriculture, theres a dive into agriculture farm fields, if theyre in civil government they might
getting into feeds of all the new roads or buildings in the area, or the imagery itself. Look at Google, uses the imagery itself to then inform and improve the maps. But, however, they are digesting it is data feeds, and thats why we liken it
to a Bloomberg Terminal. A lot of people are familiar with Bloomberg Terminal which provides data feeds into peoples workflows and helps them make smarter decisions. Whether thats the imagery itself, or the analytics on top of that, we
are doing the same. And, just like Bloomberg, its a high-growth, high-margin business and its high-stickiness. That means when people have integrated this data into their workflow, its very hard to switch because now theyre
calibrated to that and so on. And so thats why we think of it as Bloomberg. There are some differences with Bloomberg, obviously, theres limitations, and we are not talking about physical term, of course, were talking about web
interface. But the main differences are that whereas Bloomberg may aggregate open source data thats available, financial data, and provides data analytics on top of that, we have a proprietary data set from our 200 satellite fleet that enable
this and we service multiple vertical markets, not just finance, although finance is a market we serve. And so, we think of it as Bloomberg plus plus, obviously thats a great analogy for us, as Bloomberg is a very successful business. But, I
really think in all honesty, it does look and feel like a Bloomberg data business and not a satellite business, right, were not selling satellite data feeds.
There was one final slide I have just to tee up our discussion, just to talk just a tiny bit about some of the use cases that people actually get value out of
our imagery, you know, its not just for giggles. This is imagery that it is really enabling smarter decisions, economically and so on. So in agriculture, our data enables improvements of crop yields. So for each three-by-three meter box, we
can actually tell with our spectral bands, how well the crop is doing. Is it wheat, or is it soy, and how well is it doing? That enables it, when we do that across the whole farmers field, we can determine this areas got blight, or this area
needs more fertilizer, or this is when the crop is ready to be harvested. All that sort of intelligence helps whats called precision, or digital, agriculture, enables improvements of crop viewers of 10s of percent, which is a big deal with
agriculture. And we can do this for all the farmers fields across the entire world every day. So big companies, like Corteva and others, are using our data across wide areas. Cant be done with drones because they just dont have the
coverage. Cant be done with the high-resolution zooming satellites that everyone else has. Only we have the scan of the whole Earth every day, and agriculture is 25% of the land mass of the Earth. Just to mention a couple of others, and I
wont go to the same length of depth, but the defense and intelligence, we enable countries to cease new threats around the corner that they didnt know. We discovered a missile site in eastern Iran that people didnt know about. We
found new threats and thats super important for countries to know about, all threats are
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